[HTML][HTML] Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review
The recent advancements made in the realms of Artificial Intelligence (AI) and Artificial
Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to …
Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to …
[HTML][HTML] Groundwater level prediction using machine learning models: A comprehensive review
Developing accurate soft computing methods for groundwater level (GWL) forecasting is
essential for enhancing the planning and management of water resources. Over the past two …
essential for enhancing the planning and management of water resources. Over the past two …
Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
Artificial intelligence (AI) will transform business practices and industries and has the
potential to address major societal problems, including sustainability. Degradation of the …
potential to address major societal problems, including sustainability. Degradation of the …
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear …
It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate
and reliable groundwater level forecasts, which are an important tool for sustainable …
and reliable groundwater level forecasts, which are an important tool for sustainable …
[PDF][PDF] Deep learning based modeling of groundwater storage change
The understanding of water resource changes and a proper projection of their future
availability are necessary elements of sustainable water planning. Monitoring GWS change …
availability are necessary elements of sustainable water planning. Monitoring GWS change …
A hybrid deep learning algorithm and its application to streamflow prediction
Process-based streamflow prediction is subjected to large uncertainties in model
parameters and parameterizations related to the complex processes involved in streamflow …
parameters and parameterizations related to the complex processes involved in streamflow …
Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity
Long-term forecasting of any hydrologic phenomena is essential for strategic environmental
planning, hydrologic and other forms of structural design, agriculture, and water resources …
planning, hydrologic and other forms of structural design, agriculture, and water resources …
Deep learning shows declining groundwater levels in Germany until 2100 due to climate change
In this study we investigate how climate change will directly influence the groundwater
resources in Germany during the 21st century. We apply a machine learning groundwater …
resources in Germany during the 21st century. We apply a machine learning groundwater …
Reconstruction of missing groundwater level data by using Long Short-Term Memory (LSTM) deep neural network
Monitoring groundwater level (GWL) over long time periods is critical in understanding the
variability of groundwater resources in the present context of global changes. However, in …
variability of groundwater resources in the present context of global changes. However, in …
Deep learning data-intelligence model based on adjusted forecasting window scale: application in daily streamflow simulation
Streamflow forecasting is essential for hydrological engineering. In accordance with the
advancement of computer aids in this field, various machine learning (ML) models have …
advancement of computer aids in this field, various machine learning (ML) models have …